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Data Science 1 Year Masters – Complete compilation of my checklist to learn data science from beginners to experts in just one year with time travel stories. Enjoy learning!
Finally, this article is what we have all been waiting for. In this complete article, we will discuss how a complete beginner can start their journey in the vast field of machine learning and data science, from learning basic concepts and writing basic code to cracking interviews and gaining experience over time. There is a lot of content on the internet and in books, but what to read and what not to read? Totally confused! Let’s back ourselves up and go back in time to start over.
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If you are a beginner and want to learn data science with passion then trust me this article will definitely help you to build your machine learning and data science learning plan.
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Before that, just a little tip, this article will be short (😅 not so short), simple and to the point, I will discuss the correct approaches I followed if I were to start again and become a data over time. Cool time travel storyteller!
Thanks Rick and Morty for bringing us back in 2019! Now that we have a full year with us, let’s plan our full itinerary. So basically we first make a checklist of things to think about before we explore and take care of things. Below is my simple descriptive checklist:
Now the checklist will be useless until we know the best and most useful resources to start with.
Perfectly channeled to execute these two things can make me a good divisional secretary in as little as a year!
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To become a data scientist, you don’t need to be a pro-coder or 5 stars on CodeChef or TopCoder, you just need to know how to write well-optimized code in your favorite language. Especially those from diverse backgrounds with zero coding experience have become great data scientists within a year of learning to code competently.
Choose a language: Python and R programming is one of the best supported languages for Machine Learning and Data Science since 2014. In the code. The Google trend chart above shows how popular these languages were on the Google search engine. You can try both languages and explore which suits you best, and then which you think can help you more in your job profile.
Some people might learn both, but for the sake of time I would choose Python between them based on my needs. In my experience some resources for learning Python are youtube: Sentdex or Corey Schafer. Other than that, I would like to take a month package at DataCamp and try my hands-on Python lessons or for free, LearnPyhon.org will also help with that.
Anyone can get a machine learning model working with just 3-4 lines, but have you ever thought about what goes on behind the scenes? The core of a machine learning algorithm runs behind the math and makes it work for us. The libraries’ support has made our work easier, but we need to be clear about how it works and how we can build our own models.
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For that we need to be very clear with basic mathematics like linear algebra and explain to me the geometric logic of each algorithm and how to make my own model with understanding of algebra and vector system. Topics such as principal component analysis (PCA), support vector machines (SVMs) and understanding of matrix algebra and vector systems that can be used in many other mathematical models.
Second, statistics and probability are really important for understanding patterns in data and finding insights. Statistical and probability theory required for machine learning is understanding of various distributions like Gaussian distribution, binomial distribution, probability rule based on conditional probability Bayes theorem, Perito’s law etc.
Calculus also plays an important role in understanding mathematical models and required topics include differential and integral calculus, Laplacian, Jacobian, partial derivatives, directional gradients, Lagrangian distribution, etc.
Finally, algorithm and optimization problems are another very important mathematical aspect that is necessary for the computational efficiency and scalability of our machine learning algorithms. An understanding of how to write well-optimized model building or data normalization can be built well with an understanding of optimization algorithms.
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Some good resources are ISLR book or mathematics for specialization in machine learning or I can do linear algebra, probability and statistics, multivariate calculus and optimization courses on Khan Academy.
There are tons of great blogs published by very experienced people every day. I can pick a few to read each day. Now I have to plan how many blogs/articles I can digest in a day without breaking the flow and maybe understand what’s happening in the world of data science or get familiar with some technical AI news or whatever. Some good publishers that regularly publish some great content are Into Data Science, Data Driven Investor, Analytics Science or KD Nuggets.
Listening to a few good podcasts can drastically improve my skills in understanding the science behind running great projects or how deeply researchers are disrupting the field of machine learning and artificial intelligence. Podcasts primarily help us build well-earned data skills to communicate our data stories to everyone. Here is a list of good podcasts to listen to regularly, of which I personally like DataFramed by Hugo-Bownie or SuperDataScience Podcast by Kirill Eremenko or maybe DataHack Radio on SoundCloud on a daily basis.
Reading Machine Learning Books “A must-read for anyone trying to get above average.” – Jim Rohn
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Reading books is one of the most important things I will do this year to improve my learning. I must say this, if you are a book lover, then books are one of the best sources to enhance your learning. I can choose to read free ebooks or buy a paper copy, either works. There are many books that one can buy and start reading, but some good books that I suggest to read:
Online MOOC courses can be a good source to learn ML and DL in less time and keep the journey interactive. I would like to follow the following courses to learn ML and DL.
Apart from these, some standard universities across the world also offer great online or offline courses for ML and Data Science. One can check them from their official website if they want to explore more details or read the related article to know more.
Skill gymnastics and practical training ‘Tell me, I forget. I remember teaching myself. Get involved and I will learn.’ -Benjamin Franklin
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Well said, if one keeps learning and not engaging with things, they cannot retain knowledge in permanent memory for a long time. Studying is a very important process for learning. I would definitely prefer to write code for every concept I learned on my local machine and push it on some Cloud or GitHub to be safe or use some good online platforms like DataCamp or Dataquest to do it on their cloud. This will not only strengthen my concepts but also help me improve my coding skills over time as I can revise anytime by looking at the code.
Working on projects has been very important to apply what I learned to the past to build a sense of how ML projects are built in the real industry. A complete list of projects you can work on DataFlair, Simplilearn or any random article on the internet can help you start doing basic projects. Second, creating a well-documented repository of my code for a public project on GitHub or any VCS would definitely help me build a good portfolio.
Participating in competitions will ultimately help me become good at coding and apply my skills to real-world problems and see where I stand in the global rankings, which can help me correct my mistakes and build a better yourself. I participate in all Kaggle, Analytics Vidya, Driven Data or HackerEarth competitions which conduct amazing global research as well as industry level competitions. But make sure you don’t overmatch!
You can read this article below to know more about my journey at Kaggle from the future (😅 don’t forget we are now in the past).
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